Science Watch

Concepts and Conceptual Structure

Douglas L. Medin University of Illinois at Urbana

ABSTRACT: Research and theory on categorization and

conceptual structure have recently undergone two major

shifts. The first shift is from the assumption that concepts

have defining properties (the classical view) to the idea

that concept representations may be based on properties

that are only characteristic or typical of category examples

(the probabilistic view). Both the probabilistic view and

the classical view assume that categorization is driven by

similarity relations. A major problem with describing cat-

egory structure in terms of similarity is that the notion of

similarity is too unconstrained to give an account of con-

ceptual coherence. The second major shift is from the idea

that concepts are organized by similarity to the idea that

concepts are organized around theories. In this article,

the evidence and rationale associated with these shifts are

described, and one means of integrating similarity-based

and theory-driven categorization is outlined.

What good are categories? Categorization involves treat-

ing two or more distinct entities as in some way equivalent

in the service of accessing knowledge and making pre-

dictions. Take psychodiagnostic categories as an example.

The need to access relevant knowledge explains why clin-

ical psychologists do not (or could not) treat each indi-

vidual as unique. Although one would expect treatment

plans to be tailored to the needs of individuals, absolute

uniqueness imposes the prohibitive cost of ignorance.

Clinicians need some way to bring their knowledge and

experience to bear on the problem under consideration,

and that requires the appreciation of some similarity or

relationship between the current situation and what has

gone before. Although clinical psychologists may or may

not use a specific categorization system, they must find

points of contact between previous situations and the

current context; that is, they must categorize. Diagnostic

categories allow clinicians to predict the efficacy of alter-

native treatments and to share their experiences with other

therapists. Yet another reason to categorize is to learn

about etiology. People who show a common manifestation

of some problem may share common precipitating con-

ditions or causes. Ironically, the only case in which cat-

egorization would not be useful is where all individuals

are treated alike; thus, categorization allows diversity.

More generally speaking, concepts and categories

serve as building blocks for human thought and behavior.

Roughly, a concept is an idea that includes all that is char-

acteristically associated with it. A category is a partitioning

or class to which some assertion or set of assertions might

apply. It is tempting to think of categories as existing in

the world and of concepts as corresponding to mental

representations of them, but this analysis is misleading.

It is misleading because concepts need not have real-world

counterparts (e.g., unicorns) and because people may im-

pose rather than discover structure in the world. I believe

that questions about the nature of categories may be psy-

chological questions as much as metaphysical questions.

Indeed, for at least the last decade my colleagues and I

have been trying to address the question of why we have

the categories we have and not others. The world could

be partitioned in a limitless variety of ways, yet people

find only a miniscule subset of possible classifications to

be meaningful. Part of the answer to the categorization

question likely does depend on the nature of the world,

but part also surely depends on the nature of the organism

and its goals. Dolphins have no use for psychodiagnostic

categories.

Given the fundamental character of concepts and

categories, one might think that people who study con-

cepts would have converged on a stable consensus with

respect to conceptual structure. After all, Plato and Ar-

istotle had quite a bit to say about concepts, medieval

philosophers were obsessed with questions about univer-

sals and the essence of concepts, and concept represen-

tation remains as a cornerstone issue in all aspects of

cognitive science. However, we have neither consensus

nor stability. The relatively recent past has experienced

at least one and probably two major shifts in thought

about conceptual structure, and stability is the least salient

attribute of the current situation. In the remainder of this

article, I will briefly describe these shifts and then outline

some ways of integrating the strong points of the various

views.

The First Shift: Classical Versus

Probabilistic Views

It is difficult to discuss concepts without bringing in the

notion of similarity at some point. For example, a com-

mon idea is that our classification system tends to max-

imize within-category similarity relative to between-cat-

egory similarity. That is, we group things into categories

because they are similar. It will be suggested that alter-

December 1989

• American Psychologist

Vol. 44, No. 12, 1469-1481

1469

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native views of conceptual structure are associated with

distinct (though sometimes implicit) theories of the nature

of similarity.

The Classical View

The idea that all instances or examples of a category have

some fundamental characteristics in common that de-

termine their membership is very compelling. The clas-

sical view of concepts is organized around this notion.

The classical view assumes that mental representations

of categories consist of summary lists of features or prop-

erties that individually are necessary for category mem-

bership and collectively are sufficient to determine cate-

gory membership. The category triangle meets these cri-

teria. All triangles are closed geometric forms with three

sides and interior angles that sum to 180 degrees. To see

if something is a triangle one has only to check for these

three properties, and if any one is missing one does not

have a triangle.

What about other concepts? The classical view sug-

gests that all categories have defining features. A particular

person may not know what these defining features are

but an expert certainly should. In our 1981 book, Cate-

gories and Concepts, Ed Smith and I reviewed the status

of the classical view as a theory of conceptual structure.

We concluded that the classical view was in grave trouble

for a variety of reasons. Many of the arguments and

counterarguments are quite detailed, but the most serious

problems can be easily summarized:

1. Failure to specify defining features. One glaring

problem is that even experts cannot come up with defin-

ing features for most lexical concepts (i.e., those reflected

in our language). People may believe that concepts have

necessary or sufficient features (McNamara & Sternberg,

1983), but the features given as candidates do not hold

up to closer scrutiny. For example, a person may list

"made of wood" as a necessary property for violins, but

not all violins are made of wood. Linguists, philosophers,

biologists, and clinical psychologists alike have been un-

able to supply a core set of features that all examples of

a concept (in their area of expertise) necessarily must

share.

2, Goodness of example effects. According to the

classical view, all examples of a concept are equally good

because they all possess the requisite defining features.

Experience and (by now) a considerable body of research

undermines this claim. For example, people judge a robin

to be a better example of bird than an ostrich is and can

answer category membership questions more quickly for

good examples than for poor examples (Smith, Shoben,

& Rips, 1974). Typicality effects are nearly ubiquitous

The research described in this article was supported in part by National

Science Foundation Grant No. BNS 84-19756 and by National Library

of Medicine Grant No. LM 04375. Brian Ross, Edward Shoben, Ellen

Markman, Greg Oden, and Dedre Gentner provided helpful comments

on an earlier draft of the article.

Correspondence concerning this article should be addressed to

Douglas L. Medin, who is now at Department of Psychology, University

of Michigan, 330 Packard Road, Ann Arbor, MI 48104.

(for reviews, see Medin & Smith, 1984; Mervis & Rosch,

1981; Oden, 1987); they hold for the artistic style (Hartley

& Homa, 1981), chess (Goldin, 1978), emotion terms

(Fehr, 1988; Fehr & Russell, 1984), medical diagnosis

(Arkes & Harkness, 1980), and person perception (e.g.,

Cantor & Mischel, 1977).

Typicality effects are not, in principle, fatal for the

classical view. One might imagine that some signs or fea-

tures help to determine the presence of other (defining)

features. Some examples may have more signs or clearer

signs pointing the way to the defining properties, and this

might account for the difference in goodness of example

judgments or response times. This distinction between

identification procedures (how one identifies an instance

of a concept) and a conceptual core (how the concept

relates to other concepts) may prove useful if it can be

shown that the core is used in some other aspect of think-

ing. It seems, however, that this distinction serves more

to insulate the classical view from empirical findings, and

Smith, Rips, and Medin (1984) argued that there are no

sharp boundaries between core properties and those used

for purposes of identification.

3. Unclear cases. The classical view implies a pro-

cedure for unambiguously determining category mem-

bership; that is, check for defining features. Yet there are

numerous cases in which it is not clear whether an ex-

ample belongs to a category. Should a rug be considered

furniture? What about a clock or radio? People not only

disagree with each other concerning category membership

but also contradict themselves when asked about mem-

bership on separate occasions (Barsalou, 1989; Bellezza,

1984; McCloskey & Glucksberg, 1978).

These and other problems have led to disenchant-

ment with the classical view of concepts. The scholarly

consensus has shifted its allegiance to an alternative, the

probabilistic view.

The Probabilistic View

The rejection of the classical view of categories has been

associated with the ascendance of the probabilistic view

of category structure (Wittgenstein, 1953). This view

holds that categories are "fuzzy" or ill-defined and that

categories are organized around a set of properties or

clusters of correlated attributes (Rosch, 1975) that are

only characteristic or typical of category membership.

Thus, the probabilistic view rejects the notion of defining

features.

The most recent edition of the Diagnostic and Sta-

tistical Manual of Mental Disorders (DSM-IIIR, Amer-

ican Psychiatric Association, 1987) uses criteria based on

lists of characteristic symptoms or features to describe

diagnostic categories and thereby endorses the probabi-

listic view. For example, a diagnosis of depression can be

made if a dysphoric mood and any five of a set of nine

symptoms are present nearly every day for a period of at

least two weeks. Thus, two people may both be categorized

as depressed and share only a single one of the nine char-

acteristic symptoms[

The probabilistic view is perfectly at home with the

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typicality effects that were so awkward for the classical

view. Membership in probabilistic categories is naturally

graded, rather than all or none, and the better or more

typical members have more characteristic properties than

the poorer ones. It is also easy to see that the probabilistic

view may lead to unclear cases. Any one example may

have several typical properties of a category but not so

many that it clearly qualifies for category membership.

In some pioneering work aimed at clarifying the

structural basis of fuzzy categories, Rosch and Mervis

(1975) had subjects list properties of exemplars for a va-

riety of concepts such as bird, fruit, and tool. They found

that the listed properties for some exemplars occurred

frequently in other category members, whereas others had

properties that occurred less frequently. Most important,

the more frequently an excmplar's properties appeared

within a category, the higher was its rated typicality for

that category. The correlation between number of char-

acteristic properties possessed and typicality rating was

very high and positive. For example, robins have char-

acteristic bird properties of flying, singing, eating worms,

and building nests in trees, and they are rated to be very

typical birds. Penguins have none of these properties, and

they are'rated as very atypical birds. In short, the Rosch

and Mervis work relating typicality to number of char-

acteristic properties put the probabilistic view on fairly

firm footing.

1. Mental representations of probabilistic view cat-

egories. If categories arc not represented in terms of

definitions, what form do our mental representations

take? The term, probabilistic view, seems to imply that

people organize categories via statistical reasoning. Ac-

tually, however, there is a more natural interpretation of

fuzzy categories. Intuitively, probabilistic view categories

are organized according to a family resemblance principle.

A simple form of summary representation would be an

example or ideal that possessed all of the characteristic

features of a category. This summary representation is

referred to as the prototype, and the prototype can be

used to decide category membership. If some candidate

example is similar enough to the prototype for a category,

then it will be classified as a member of that category.

The general notion is that, based on experience with ex-

amples of a category, people abstract out the central ten-

dency or prototype that becomes the summary mental

representation for the category.

A more radical principle of mental representation,

which is also consistent with fuzzy categories, is the ex-

emplar view (Smith & Medin, 1981). The exemplar view

denies that there is a single summary representation and

instead claims that categories are represented by means

of examples. In this view, clients may be diagnosed as

suicidal, not because they are similar to some prototype

of a suicidal person, but because they remind the clinician

of a previous client who was suicidal.

A considerable amount of research effort has been

aimed at contrasting exemplar and prototype represen-

tations (see Allen, Brooks, Norman, & Rosenthal, 1988;

Estes, 1986a, 1986b; Medin, 1986; Medin & Smith, 1984;

Nosofsky, 1987, 1988a; and Odcn, 1987). Genero and

Cantor (1987) suggested that prototypes serve untrained

diagnosticians well but that trained diagnosticians may

find exemplars to be more helpful. For my present pur-

poses, however, I will blur over this distinction to note

that both prototype and exemplar theories rely on roughly

the same similarity principle. That is, category member-

ship is determined by whether some candidate is suffi-

ciently similar either to the prototype or to a set of en-

coded examples, where similarity is based on matches

and mismatches of independent, equally abstract, fea-

tures.

2. Probabilistic view and similarity. To give mean-

ing to thc claim that categorization is based on similarity,

it is important to be specific about what one means by

similarity. Although the consensus is not uniform, I be-

lieve that the modal model of similarity with respect to

conceptual structure can be summarized in terms of the

four assumptions as follows: (a) Similarity between two

things increases as a function of the number of features

or properties they share and decreases as a function of

mismatching or distinctive features. (b) These features

can be treated as independent and additive. (c) The fea-

tures determining similarity are all roughly the same level

of abstractness (as a special case they may be irreducible

primitives). (d) These similarity principles are sufficient

to describe conceptual structure, and therefore, a concept

is more or less equivalent to a list of its features. This

theory of similarity is very compatible with the notion

that categories arc organized around prototypes. None-

theless, I will later argue that each of these assumptions

is wrong or misleading and that to understand conceptual

structure theories of similarity are needed that reject each

of these assumptions. Before outlining an alternative set

of similarity assumptions, however, I will first describe a

set of observations that motivate the second, still more

recent, shift in thinking concerning conceptual structure.

Problems for Probabilistic View Theories

Problems for Prototypes

Although the general idea that concepts are organized

around prototypes remains popular, at a more specific,

empirical level, prototype theories have not fared very

well. First of all, prototype theories treat concepts as con-

text-independent. Roth and Shoben (1983), however, have

shown that typicality judgments vary as a function of

particular contexts. For example, tea is judged to be a

more typical beverage than milk in the context of sec-

retaries taking a break, but this ordering reverses for the

context of truck drivers taking a break. Similarly, Shoben

and I (Medin & Shoben, 1988) noted that the typicality

of combined concepts cannot be predicted from the typ-

icality of the constituents. As an illustrative example,

consider the concept of spoon. People rate small spoons

as more typical spoons than large spoons, and metal

spoons as more typical spoons than wooden spoons. If

the concept spoon is represented by a prototypic spoon,

then a small metal spoon should be the most typical

December 1989

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spoon, followed by small wooden and large metal spoons,

and large wooden spoons should be the least typical. In-

stead, people find large wooden spoons to be more typical

spoons than either small wooden spoons or large metal

spoons (see also Malt & Smith, 1983). The only way for

a prototype model to handle these results is to posit mul-

tiple prototypes. But this strategy creates new problems.

Obviously one cannot have a separate prototype for every

adjective noun combination because there are simply too

many possible combinations. One might suggest that there

are distinct subtypes for concepts like

spoon,

but one

would need a theory describing how and when subtypes

are created. Current prototype models do not provide

such a theory. A third problem for prototype theories

grows out of Barsalou's work (1985, 1987) on goal-derived

categories such as "things to take on a camping trip" and

"foods to eat while on a diet." Barsalou has found that

goal-derived categories show the same typicality effects

as other categories. The basis for these effects, however,

is not similarity to an average or prototype but rather

similarity to an ideal. For example, for the category of

things to eat while on a diet, typicality ratings are deter-

mined by how closely an example conforms to the ideal

of zero calories.

Laboratory studies of categorization using artificially

constructed categories also raise problems for prototypes.

Normally many variables relevant to human classification

are correlated and therefore confounded with one another.

The general rationale for laboratory studies with artifi-

cially created categories is that one can isolate some vari-

able or set of variables of interest and unconfound some

natural correlations. Salient phenomena associated with

fuzzy categories are observed with artificially constructed

categories, and several of these are consistent with pro-

totype theories. For example, one observes typicality ef-

fects in learning and on transfer tests using both correct-

ness and reaction time as the dependent variable (e.g.,

Rosch & Mervis, 1975). A striking phenomenon, readily

obtained, is that the prototype for a category may be clas-

sified more accurately during transfer tests than are the

previously seen examples that were used during original

category learning (e.g., Homa & Vosburgh, 1976; Medin

& Schaffer, 1978; Peterson, Meagher, Chait, & Gillie,

1973).

Typicality effects and excellent classification of pro-

totypes are consistent with the idea that people are learn-

ing these ill-defined categories by forming prototypes.

More detailed analyses, however, are more problematic.

Prototype theory implies that the only information ab-

stracted from categories is the central tendency. A pro-

totype representation discards information concerning

category size, the variability of the examples, and infor-

mation concerning correlations of attributes. The evi-

dence suggests that people are sensitive to all three of

these types of information (Estes, 1986b; Flannagan,

Fried, & Holyoak, 1986; Fried & Holyoak, 1984; Medin,

Altom, Edelson, & Freko, 1982; Medin & Schaffer, 1978).

An example involving correlated attributes pinpoints part

of the problem. Most people have the intuition that small

birds are much more likely to sing than large birds. This

intuition cannot be obtained from a single summary pro-

totype for birds. The fact that one can generate large

numbers of such correlations is a problem for the idea

that people reason using prototypes. More generally, pro-

totype representations seem to discard too much infor-

mation that can be shown to be relevant to human cat-

egorizations.

Yet another problem for prototypes is that they make

the wrong predictions about which category structures

should be easy or difficult to learn. One way to concep-

tualize the process of classifying examples on the basis

of similarity to prototypes is that it involves a summing

of evidence against a criterion. For example, if an instance

shows a criterial sum of features (appropriately weighted),

then it will be classified as a bird, and the more typical a

member is of the category, the more quickly the criterion

will be exceeded. The key aspect of this prediction is that

there must exist some additive combination of properties

and their weights that can be used to correctly assign

instances as members or nonmembers. The technical

term for this constraint is that categories must be linearly

separable (Sebestyn, 1962). For a prototype process to

work in the sense of accepting all members and rejecting

all nonmembers, the categories must be linearly separable.

If linear separability acts as a constraint on human

categorization, then with other factors equal, people

should find it easier to learn categories that are linearly

separable than categories that are not linearly separable.

To make a long story short, however, studies employing

a variety of stimulus materials, category sizes, subject

populations, and instructions have failed to find any ev-

idence that linear separability acts as a constraint on hu-

man classification learning (Kemler-Nelson, 1984; Medin

& Schwanenflugel, 1981; see also Shepard, Hovland, &

Jenkins, 1961).

The cumulative effect of these various chunks of ev-

idence has been to raise serious questions concerning the

viability of prototype theories. Prototype theories imply

constraints that are not observed in human categoriza-

tion, predict insensitivity to information that people

readily use, and fail to reflect the context sensitivity that

is evident in human categorization. Rather than getting

at the character of human conceptual representation,

prototypes appear to be more of a caricature of it. Ex-

emplar models handle some of these phenomena, but they

fail to address some of the most fundamental questions

concerning conceptual structure.

Exemplar-Based

Theories

The problems just described hold not only for prototype

theories in particular but also for any similarity-based

categorization model that assumes that the constituent

features are independent and additive. To give but one

example, one could have an exemplar model of catego-

rization that assumes that, during learning, people store

examples but that new examples are classified by "com-

puting" prototypes and determining the similarity of the

novel example to the newly constructed prototypes. In

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short, the central tendency would be abstracted (and other

information discarded) at the time of retrieval rather than

at the time of storage or initial encoding. Such a model

would inherit all the shortcomings of standard prototype

theories.

Some exemplar storage theories do not endorse the

notion of feature independence (Hintzman, 1986; Medin

& Schaffer, 1978), or they assume that classification is

based on retrieving only a subset of the stored examples

(presumably the most similar ones or, as a special case,

the most similar one). The idea that retrieval is limited,

similarity-based, and context-sensitive is in accord with

much of the memory literature (e.g., Tulving, 1983). In

addition, these exemplar models predict sensitivity to

category size, instance variability, context, and correlated

attributes. It is my impression that in head-to-head com-

petition, exemplar models have been substantially more

successful than prototype models (Barsalou & Medin,

1986; Estes, 1986b; Medin & Ross, 1989; Nosofsky,

1988a, 1988b; but see Homa, 1984, for a different

opinion).

Why should exemplar models fare better than pro-

totype models? One of the main functions of classification

is that it allows one to make inferences and predictions

on the basis of partial information (see Anderson, 1988).

Here I am using classification loosely to refer to any means

by which prior (relevant) knowledge is brought to bear,

ranging from a formal classification scheme to an idio-

syncratic reminding of a previous case (which, of course,

is in the spirit of exemplar models; see also Kolodner,

1984). In psychotherapy, clinicians are constantly making

predictions about the likelihood of future behaviors or

the efficacy of a particular treatment based on classifi-

cation. Relative to prototype models, exemplar models

tend to be conservative about discarding information that

facilitates predictions. For instance, sensitivity to corre-

lations of properties within a category enables finer pre-

dictions: From noting that a bird is large, one can predict

that it cannot sing. It may seem that exemplar models

do not discard any information at all, but they are in-

complete without assumptions concerning retrieval or

access. In general, however, the pairs of storage and re-

trieval assumptions associated with exemplar models

preserve much more information than prototype models.

In a general review of research on categorization and

problem-solving, Brian Ross and I concluded that ab-

straction is both conservative and tied to the details of

specific examples in a manner more in the spirit of ex-

emplar models than prototype models (Medin & Ross,

1989).

Unfortunately, context-sensitive, conservative cate-

gorization is not enough. The debate between prototype

and exemplar models has taken place on a platform con-

structed in terms of similarity-based categorization. The

second shift is that this platform has started to crumble,

and the viability of probabilistic view theories of cate-

gorization is being seriously questioned. There are two

central problems. One is that probabilistic view theories

do not say anything about why we have the categories we

have. This problem is most glaringly obvious for exemplar

models that appear to allow any set of examples to form

a category. The second central problem is with the notion

of similarity. Do things belong in the same category be-

cause they are similar, or do they seem similar because

they are in the same category?

Does

Similarity Explain Categorization?

1. Flexibility. Similarity is a very intuitive notion. Un-

fortunately, it is even more elusive than it is intuitive.

One problem with using similarity to define categories is

that similarity is too flexible. Consider, for example,

Tversky's (1977) influential contrast model, which defines

similarity as a function of common and distinctive fea-

tures weighted for salience or importance. According to

this model, similarity relationships will depend heavily

on the particular weights given to individual properties

or features. For example, a zebra and a barberpole would

be more similar than a zebra and a horse if the feature

"striped" had sufficient weight. This would not necessarily

be a problem if the weights were stable. However, Tversky

and others have convincingly shown that the relative

weighting of a feature (as well as the relative importance

of matching and mismatching features) varies with the

stimulus context, experimental task (Gati & Tversky,

1984; Tversky, 1977), and probably even the concept un-

der consideration (Ortony, Vondruska, Foss, & Jones,

1985). For example, common properties shared by a pair

of entities may become salient only in the context of some

third entity that does not share these properties.

Once one concedes that similarity is dynamic and

depends on some (not well-understood) processing prin-

ciples, earlier work on the structural underpinnings of

fuzzy categories can be seen in a somewhat different light.

Recall that the Rosch and Mervis (1975) studies asked

subjects to list attributes or properties of examples and

categories. It would be a mistake to assume that people

had the ability to read and report their mental represen-

tations of concepts in a veridical manner. Indeed Keil

(1979, 1981) pointed out that examples like robin and

squirrel shared many important properties that almost

never show up in attribute listings (e.g., has a heart,

breathes, sleeps, is an organism, is an object with bound-

aries, is a physical object, is a thing, can be thought about,

and so on). In fact, Keil argued that knowledge about

just these sorts of predicates, referred to as ontological

knowledge (Sommers, 1971), serves to organize children's

conceptual and semantic development. For present pur-

poses, the point is that attribute listings provide a biased

sample of people's conceptual knowledge. To take things

a step further, one could argue that without constraints

on what is to count as a feature, any two things may be

arbitrarily similar or dissimilar. Thus, as Murphy and I

(Murphy & Medin, 1985) suggested, the number of prop-

erties that plums and lawn mowers have in common could

be infinite: Both weigh less than 1000 Kg, both are found

on earth, both are found in our solar system, both cannot

hear well, both have an odor, both are not worn by ele-

phants, both are used by people, both can be dropped,

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and so on (see also Goodman, 1972; Watanabe, 1969).

Now consider again the status of attribute listings. They

represent a biased subset of stored or readily inferred

knowledge. The correlation of attribute listings with typ-

icality judgments is a product of such knowledge and a

variety of processes that operate on it. Without a theory

of that knowledge and those processes, it simply is not

clear what these correlations indicate about mental rep-

resentations.

The general point is that attempts to describe cat-

egory structure in terms of similarity will prove useful

only to the extent that one specifies which principles de-

termine what is to count as a relevant property and which

principles determine the importance of particular prop-

erties. It is important to realize that the explanatory work

is being done by the principles which specify these con-

straints rather than the general notion of similarity. In

that sense similarity is more like a dependent variable

than an independent variable.

2. Attribute matching and categorization.

The

modal model of similarity summarized in Table 1 invites

one to view categorization as attribute matching. Al-

though that may be part of the story, there are several

ways in which the focus on attribute matching may be

misleading. First of all, as Armstrong, Gleitman, and

Gleitman (1983) emphasized, most concepts are not a

simple sum of independent features. The features that

are characteristically associated with the concept

bird

are

just a pile of bird features unless they are held together

in a "bird structure." Structure requires both attributes

and

relations

binding the attributes together. Typical bird

features (laying eggs, flying, having wings and feathers,

building nests in trees, and singing) have both an internal

structure and an external structure based on interproperty

relationships. Building nests is linked to laying eggs, and

building nests in trees poses logistical problems whose

solution involves other properties such as having wings,

flying, and singing. Thus, it makes sense to ask why birds

have certain features (e.g., wings and feathers). Although

people may not have thought about various interproperty

relationships, they can readily reason with them. Thus,

one can answer the question of why birds have wings and

feathers (i.e., to fly).

In a number of contexts, categorization may be more

like problem solving than attribute matching. Inferences

and causal attributions may drive the categorization pro-

cess. Borrowing again from work by Murphy and me

(1985), "jumping into a swimming pool with one's clothes

on" in all probability is not associated directly with the

concept

intoxicated.

However, observing this behavior

might lead one to classify the person as drunk. In general,

real world knowledge is used to reason about or explain

properties, not simply to match them. For example, a

teenage boy might show many of the behaviors associated

with an eating disorder, but the further knowledge that

the teenager is on the wrestling team and trying to make

a lower weight class may undermine any diagnosis of a

disorder.

3. Summary.

It does not appear that similarity, at

least in the form it takes in current theories, is going

be at all adequate to explain categorization. Similarity

may be a byproduct of conceptual coherence rather than

a cause. To use a rough analogy, winning basketball teams

have in common scoring more points than their oppo-

nents, but one must turn to more basic principles to ex-

plain why they score more points. One candidate for a

set of deeper principles is the idea that concepts are or-

ganized around theories, and theories provide conceptual

coherence. In the next section, I will briefly summarize

some of the current work on the role of knowledge struc-

tures and theories in categorization and then turn to a

form of rapprochement between similarity and knowl-

edge-based categorization principle.

The Second Shift: Concepts as Organized

by Theories

Knowledge-Based Categorization

It is perhaps only a modest exaggeration to say that sim-

ilarity gets at the shadow rather than the substance of

concepts. Something is needed to give concepts life, co-

herence, and meaning. Although many philosophers of

science have argued that observations are necessarily the-

ory-labeled, only recently have researchers begun to stress

that the organization of concepts is knowledge-based and

driven by theories about the world (e.g., Carey, 1985; S.

Gelman, 1988; S. Gelman & Markman, 1986a, 1986b;

Keil, 1986, 1987; Keil & Kelly, 1987; Lakoff, 1987;

Markman, 1987; Massey & R. Gelman, 1988; Murphy

& Medin, 1985; Oden, 1987; Rips, 1989; Schank, Collins,

& Hunter, 1986; and others).

The primary differences between the similarity-

based and theory-based approaches to categorization are

summarized in Table 1, taken from Murphy and Medin

(1985). Murphy and Medin suggested that the relation

between a concept and an example is analogous to

the

relation between theory and data. That is, classification

is not simply based on a direct matching of properties of

the concept with those in the example, but rather requires

that the example have the right "explanatory relation-

ship" to the theory organizing the concept. In the case of

a person diving into a swimming pool with his or her

clothes on, one might try to reason back to either causes

or predisposing conditions. One might believe that having

too

much to drink impairs judgment and that going into

the pool shows poor judgment. Of course, the presence

of other information, such as the fact that another person

who cannot swim has fallen into the pool, would radically

change the inferences drawn and, as a consequence,

the

categorization judgment.

One of the more promising aspects of the theory-

based approach is that it begins to address the question

of why we have the categories we have or why categories

are sensible. In fact, coherence may be achieved in the

absence of any obvious source of similarity among ex-

amples. Consider the category comprised of children,

money, photo albums, and pets. Out of context the cat-

egory seems odd. If one's knowledge base is enriched to

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I II I

Table 1

Comparison of Two Approaches to Concepts

III

Aspect of

conceptual theory Similarity-based approach Theory-based approach

Concept representation

Category definition

Units of analysis

Similarity

structure, attribute lists,

correlated attributes

Various similarity metrics,

summation of attributes

Attributes

Categorization basis

Attribute matching

Weighting of attributes

Cue validity, salience

Interconceptual

structure

Hierarchy

based on shared

attributes

Conceptual Feature

accretion

development

include the fact that the category represents "things to

take out of one's house in case of a fire," the category

becomes sensible (Barsalou, 1983). In addition, one could

readily make judgments about whether new examples

(e.g., personal papers) belonged to the category, judgments

that would not be similarity based.

Similarity effects can be overridden by theory-related

strategies even in the judgments of young children. That

fact was very nicely demonstrated by Gelman and Mark-

man (1986a) in their studies of induction. Specifically,

they pitted category membership against perceptual sim-

ilarity in an inductive inference task. Young children were

taught that different novel properties were true of two

examples and then were asked which property was also

true of a new example that was similar to one alternative

but belonged to a different category, and one that was

perceptually different from the other examples but be-

longed to the same category. For example, children might

be taught that a (pictured) flamingo feeds its baby mashed-

up food and that a (pictured) bat feeds its baby milk, and

then they might be asked how a (pictured) owl feeds its

baby. The owl was more perceptually similar to the bat

than to the flamingo, but even four-year-olds made in-

ferences on the basis of category membership rather than

similarity.

Related work by Susan Carey and Frank Keil shows

that children's biological theories guide their conceptual

development. For example, Keil has used the ingenious

technique of describing transformations or changes such

as painting a horse to look like a zebra to examine the

extent to which category membership judgments are

controlled by superficial perceptual properties. Biological

theories determine membership judgments quite early on

(Keil, 1987; Keil & Kelly, 1987). Rips (1989) has used

the same technique to show that similarity is neither nee-

Correlated attributes plus underlying principles that

determine

which correlations are noticed

An explanatory principle common to category

members

Attributes plus explicitly represented relations

attributes and concepts

Matching plus inferential processes supplied

underlying principles

Determined in part by importance in the underlying

principles

Network formed by causal and explanatory links, as

well as sharing of properties picked out as

relevant

Changing organization and explanations

concepts as a result of world knowledge

II IIII

essary nor sufficient to determine category membership.

It even appears to be the case that theories can affect

judgments of similarity. For example, Medin and Shoben

(1988) found that the terms

white hair

and

grey hair

were

judged to be more similar than grey

hair and black hair,

but that the terms

white clouds

and

grey clouds

were

judged as less similar than

grey clouds and black clouds.

Our interpretation is that white and grey hair are linked

by a theory (of aging) in a way that white and grey clouds

are not.

The above observations are challenging for defenders

of the idea that similarity drives conceptual organization.

In fact, one might wonder if the notion of similarity is so

loose and unconstrained that we might be better offwith-

out it. Goodman (1972) epitomized this attitude by calling

similarity "a pretender, an imposter, a quack" (p. 437).

After reviewing some reasons to continue to take simi-

larity seriously, I outline one possible route for integrating

similarity-based and theory-based categorization.

The

Need for Similarity

So far I have suggested that similarity relations do not

provide conceptual coherence but that theories do. Be-

cause a major problem with similarity is that it is so un-

constrained, one might ask what constrains theories. If

we cannot identify constraints on theories, that is, say

something about why we have the theories we have and

not others, then we have not solved the problem of co-

herence: It simply has been shifted to another level. Al-

though I believe we can specify some general properties

of theories and develop a psychology of explanation (e.g.,

Abelson & Lalljee, 1988; Einhorn & Hogarth, 1986; Hil-

ton & Slugoski, 1986; Leddo, Abelson, & Gross, 1984),

I equally believe that a constrained form ofslmilarity will

play an important role in our understanding of human

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concepts. This role is not to provide structure so much

as it is to guide learners toward structure.

The impact of more direct perceptual similarity on

the development of causal explanations is evident in the

structure of people's naive theories. Frazer's (1959) cross-

cultural analysis of belief systems pointed to the ubiquity

of two principles, homeopathy and contagion. The prin-

ciple of homeopathy is that causes and effects tend to be

similar. One manifestation of this principle is homeo-

pathic medicine, in which the cure (and the cause) are

seen to resemble the symptoms. In the Azande culture,

for example, the cure for ringworm is to apply fowl's

excrement because the excrement looks like the ring-

worm. Schweder (1977) adduced strong support for the

claim that resemblance is a fundamental conceptual tool

of everyday thinking in all cultures, not just so-called

primitive cultures.

Contagion is the principle that a cause must have

some form of contact to transmit its effect. In general,

the more contiguous (temporally and spatially similar)

events are in time and space, the more likely they are to

be perceived as causally related (e.g., Dickinson, Shanks,

& Evenden, 1984; Michotte, 1963). People also tend to

assume that causes and effects should be of similar mag-

nitude. Einhorn and Hogarth (1986) pointed out that the

germ theory of disease initially met with great resistance

because people could not imagine how such tiny organ-

isms could have such devastating effects.

It is important to recognize that homeopathy and

contagion often point us in the right direction. Immu-

nization can be seen as a form of homeopathic medicine

that has an underlying theoretical principle to support it.

My reading of these observations, however, is not that

specific theoretical (causal) principles are constraining

similarity but rather that similarity (homeopathy and

contagion) acts as a constraint on the search for causal

explanations. Even in classical conditioning studies, the

similarity of the conditioned stimulus and the uncondi-

tioned stimulus can have a major influence on the rate

of conditioning (Testa, 1974). Of course, similarity must

itself be constrained for terms like homeopathy to have

a meaning. Shortly, I will suggest some constraints on

similarity as part of an effort to define a role for similarity

in conceptual development.

Similarity is likely to have a significant effect on ex-

planations in another way. Given the importance of sim-

ilarity in retrieval, it is likely that explanations that are

applied to a novel event are constrained by similar events

and their associated explanations. For example, Read

(1983) found that people may rely on single, similar in-

stances in making causal attributions about behaviors.

Furthermore, Ross (1984) and Gentner and Landers

(1985) have found that superficial similarities and not

just similarity with respect to deeper principles or rela-

tions play a major role in determining the remindings

associated with problem solving and the use of analogy.

In brief, it seems that similarity cannot be banished

from the world of theories and conceptual structures. But

it seems to me that a theory of similarity is needed that

is quite different in character from the one summarized

in Table 1. I will suggest an alternative view of similarity

and then attempt to show its value in integrating and

explanation with respect to concepts.

Similarity and Theory in

Conceptual Structure

,4 Contrasting Similarity Model

The following are key tenets of the type of similarity the-

ory needed to link similarity with knowledge-based cat-

egorization: (a) Similarity needs to include attributes, re-

lations, and higher-order relations. (b) Properties in gen-

eral are not independent but rather are linked by a variety

of interproperty relations. (c) Properties exist at multiple

levels of abstraction. (d) Concepts are more than lists.

Properties and relations create depth or structure. Each

of the four main ideas directly conflicts with the corre-

sponding assumption of the theory of similarity outlined

earlier. In one way or another all of these assumptions

are tied to structure. The general idea I am proposing is

far from new. In the psychology of visual perception, the

need for structural approaches to similarity has been a

continuing, if not major, theme (e.g., Biederman, 1985,

1987; Palmer, 1975, 1978; Pomerantz, Sager, & Stoever,

1977). Oden and Lopes (1982) have argued that this view

can inform our understanding of concepts: "Although

similarity must function at some level in the induction

of concepts, the induced categories are not 'held together'

subjectively by the undifferentiated 'force' of similarity,

but rather by structural principles" (p. 78). Noninde-

pendence of properties and simple and higher-order re-

lations add a dimension of depth to categorization. Depth

has clear implications for many of the observations that

seem so problematic for probabilistic view theories. I turn

now to the question of how these modified similarity no-

tions may link up with theory-based categorization.

Psychological Essentialism

Despite the overwhelming evidence against the classical

view, there is something about it that is intuitively com-

pelling. Recently I and my colleagues have begun to take

this observation seriously, not for its metaphysical im-

plications but as a piece of psychological data (Medin&

Ortony, 1989; Medin & Wattenmaker, 1987; Warren-

maker, Nakamura, & Medin, 1988). One might call this

framework "psychological essentialism." The main ideas

are as follows: People act as if things (e.g., objects) have

essences or underlying natures that make them the thing

that they are. Furthermore, the essence constrains or gen-

erates properties that may vary in their centrality. One

of the things that theories do is to embody or provide

causal linkages from deeper properties to more superficial

or surface properties. For example, people in our culture

believe that the categories male and female are genetically

determined, but to pick someone out as male or female

we rely on characteristics such as hair length, height, facial

hair, and clothing that represent a mixture of secondary

sexual characteristics and cultural conventions. Although

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these characteristics are more unreliable than genetic ev-

idence, they are far from arbitrary. Not only do they have

some validity in a statistical sense, but also they are tied

to our biological and cultural conceptions of

male and

female.

It is important to note that psychological essentialism

refers not to how the world is but rather to how people

approach the world. Wastebaskets probably have no true

essence, although we may act as if they do. Both social

and psychodiagnostic categories are at least partially cul-

ture specific and may have weak if any metaphysical un-

derpinnings (see also Morey & McNamara, 1987).

If psychological essentialism is bad metaphysics, why

should people act as if things had essences? The reason

is that it may prove to be good epistomology. One could

say that people adopt an

essentialist heuristic,

namely,

the hypothesis that things that look alike tend to share

deeper properties (similarities). Our perceptual and con-

ceptual systems appear to have evolved such that the es-

sentialist heuristic is very often correct (Medin & Wat-

tenmaker, 1987; Shepard, 1984). This is true even for

human artifacts such as cars, computers, and camping

stoves because structure and function tend to be corre-

lated. Surface characteristics that are perceptually obvious

or are readily produced on feature listing tasks may not

so much constitute the core of a concept as point toward

it. This observation suggests that classifying on the basis

of similarity will be relatively effective much of the time,

but that similarity will yield to knowledge of deeper prin-

ciples. Thus, in the work of Gelman and Markman

(1986a) discussed earlier, category membership was more

important than perceptual similarity in determining in-

ductive inferences.

Related Evidence

The contrasting similarity principles presented earlier

coupled with psychological essentialism provide a frame-

work for integrating knowledge-based and similarity-

based categorization. Although it is far short of a formal

theory, the framework provides a useful perspective on

many of the issues under discussion in this article.

1. Nonindependence of features.

Earlier I men-

tioned that classifying on the basis of similarity to a pro-

totype was functionally equivalent to adding up the evi-

dence favoring a classification and applying some criterion

(at least X out of Y features). Recall also that the data

ran strongly against this idea. From the perspective cur-

rently under consideration, however, there ought to be

two ways to produce data consistent with prototype the-

ory. One would be to provide a theory that suggests the

prototype as an ideal or that makes summing of evidence

more natural. For example, suppose that the characteristic

properties for one category were as follows: It is made of

metal, has a regular surface, is of medium size, and is

easy to grasp. For a contrasting category the characteristic

properties were: It is made of rubber, has an irregular

surface, is of small size, and is hard to grasp. The cate-

gories may not seem sensible or coherent but suppose

one adds the information that the objects in one category

could serve as substitutes for a hammer. Given this new

information, it becomes easy to add up the properties of

examples in terms of their utility in supporting ham-

mering. In a series of studies using the above descriptions

and related examples, Wattenmaker, Dewey, Murphy, and

I (1986) found data consistent with prototype theory when

the additional information was supplied, and data incon-

sistent with prototype theory when only characteristic

properties were supplied. Specifically, they found that

linearly separable categories were easier to learn than

nonlinearly separable categories only when an organizing

theme was provided (see also Nakamura, 1985).

One might think that prototypes become important

whenever the categories are meaningful. That is not the

case. When themes are provided that are not compatible

with a summing of evidence, the data are inconsistent

with prototype theories. For instance, suppose that the

examples consisted of descriptions of animals and that

the organizing theme was that one category consisted of

prey and the other of predators. It is a good adaptation

for prey to be armored and to live in trees, but an animal

that is both armored and lives in trees may not be better

adapted than an animal with either characteristic alone.

Being armored and living in trees may be somewhat in-

compatible. Other studies by Wattenmaker et al. using

directly analogous materials failed to find any evidence

that linear separability (and, presumably, summing of

evidence) was important or natural. Only some kinds of

interproperty relations are compatible with a summing

of evidence, and evidence favoring prototypes may be

confined to these cases.

The above studies show that the ease or naturalness

of classification tasks cannot be predicted in terms of

abstract category structures based on distribution of fea-

tures, but rather requires an understanding of the knowl-

edge brought to bear on them, for this knowledge deter-

mines inter-property relationships. So far only a few types

of interproperty relationships have been explored in cat-

egorization, and much is to be gained from the careful

study of further types of relations (e.g., see Barr & Caplan,

1987; Chaflin & Hermann, 1987; Rips & Conrad, 1989;

Winston, Chaftin, & Herman, 1987).

2. Levels of features.

Although experimenters can

often contrive to have the features or properties com-

prising stimulus materials at roughly the same level of

abstractness, in more typical circumstances levels may

vary substantially. This fact has critical implications for

descriptions of category structure (see Barsalou & Bill-

man, 1988). This point may be best represented by an

example from some ongoing research I am conducting

with Glenn Nakamura and Ed Wisniewski. Our stimulus

materials consist of children's drawings of people, a sam-

ple of which is shown in Figure 1. There are two sets of

five drawings, one on the left and one on the right. The

task of the participants in this experiment is to come up

with a rule that could be used to correctly classify both

these drawings and new examples that might be presented

later.

One of our primary aims in this study was to ex-

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Figure 1

Children's Drawings of People Used in the

Rule Induction Studies by Nakamura,

Wisniewski, and Medin

amine the effects of different types of knowledge structures

on rule induction. Consequently, some participants were

told that one set was done by farm children and the other

by city children; some were told that one set was done

by creative children and the other by noncreative children;

and still others were told that one set was done by emo-

tionally disturbed children and the other by mentally

healthy children. The exact assignment of drawings was

counterbalanced with respect to the categories such that

half the time the drawings on the left of Figure 1 were

labeled as done by farm children and half the time the

drawings on the right were labeled as having been done

by farm children.

Although we were obviously expecting differences

in the various conditions, in some respects the most strik-

ing result is one that held across conditions. Almost with-

out exception the rules that people gave had properties

at two or three different levels of abstractness. For ex-

ample, one person who was told the drawings on the left

were done by city children gave the following rule: "The

city drawings use more profiles, and are more elaborate.

The clothes are more detailed, showing both pockets and

buttons, and the hair is drawn in. The drawings put less

emphasis on proportion and the legs and torso are off."

Another person who was told the same drawings were

done by farm children wrote: "The children draw what

they see in their normal life. The people have overalls on

and some drawings show body muscles as a result of labor.

The drawings are also more detailed. One can see more

facial details and one drawing has colored the clothes and

another one shows the body under the clothes." As one

can see, the rules typically consist of a general assertion

or assertions coupled with either an operational definition

or examples to illustrate and clarify the assertion. In some

cases these definitions or examples extend across several

levels of abstractness.

One might think that our participants used different

levels of description because there was nothing else for

them to do. That is, there may have been no low-level

perceptual features that would separate the groups. In a

followup study we presented examples one at a time and

asked people to give their rule after each example. If peo-

pie are being forced to use multiple levels of description

because simple rules will not work, then we should ob-

serve a systematic increase in the use of multiple levels

across examples. In fact, however, we observed multiple

levels of description as the predominant strategy from the

first example on. We believe that multiple levels arise

when people try to find a link between abstract explan-

atory principles or ideas (drawings reflect one's experi-

ence) and specific details of drawings.

There are several important consequences of mul-

tilevel descriptions. First of all, the relation across levels

is not necessarily a subset, superset, or a part-whole re-

lation. Most of the time one would say that the lower level

property "supports" the higher level property; for ex-

ample, "jumping into a swimming pool with one's clothes

on" supports poor judgment. This underlines the point

that categorization often involves more than a simple

matching of properties. A related point is that features

are ambiguous in the sense that they may support more

than one higher level property. When the drawings on

the right were associated with the label mentally healthy,

a common description was "all the faces are smiling."

When the label for the same drawing was noncreative, a

common description was "the faces show little variability

in expression." Finally, it should be obvious that whether

a category description is disjunctive (e.g., pig's nose or

cow's mouth or catlike ears) or conjunctive or defining

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(e.g., all have animal parts) depends on the level with

respect to which the rule is evaluated.

3. Centrality.

If properties are at different levels of

abstraction and linked by a variety of relations, then one

might imagine that some properties are more central than

others because of the role they play in conceptual struc-

ture. An indication that properties differ in their centrality

comes from a provocative study by Asch and Zukier

(1984). They presented people with trait terms that ap-

peared to be contradictory (e.g., kind and vindictive) and

asked participants if these descriptions could be resolved

(e.g., how could a person be both kind and vindictive?).

Participants had no difficulty integrating the pairs of

terms, and Asch and Zukier identified seven major res-

olution strategies. For present purposes, what is notable

is that many of the resolution strategies involve making

one trait term more central than the other one. For ex-

ample, one way of integrating

kind

and

vindictive

was to

say that the person was fundamentally evil and was kind

only in the service of vindictive ends.

In related work, Shoben and I (Medin & Shoben,

1988) showed that centrality of a property depends on the

concept of which it is a part. We asked participants to

judge the typicality of adjective noun pairs when the ad-

jective was a property that other participants judged was

not true of the noun representing the concept. For example,

our participants judged that all bananas and all boomerangs

are curved. Based on this observation, other participants

were asked to judge the typicality of a straight banana as

a banana or a straight boomerang as a boomerang. Other

instances of the 20 pairs used include

soft knife

versus

soft

diamond

and

polka dot .fire hydrant

versus

polka dot yield

sign.

For 19 of the 20 pairs, participants rated one item

of a pair as more typical than the other. Straight banana,

soft knife, and polka dot fire hydrant were rated as more

typical than straight boomerang, soft diamond, and polka

dot yield sign. In the case of boomerangs (and probably

yield signs), centrality may be driven by structure-function

correlations. Soft diamonds are probably rated as very

atypical because hardness is linked to many other prop-

erties and finding out that diamonds were soft would call

a great deal of other knowledge into question.

Most recently, Woo Kyoung Ahn, Joshua Ruben-

stein, and I have been interviewing clinical psychologists

and psychiatrists concerning their understanding of psy-

chodiagnostic categories. Although our project is not far

enough along to report any detailed results, it is clear that

the

DSM-IIIR

guidebook (American Psychiatric Asso-

ciation, 1987) provides only a skeletal outline that is

brought to life by theories and causal scenarios underlying

and intertwined with the symptoms that comprise the

diagnostic criteria. Symptoms differ in the level of ab-

stractness and the types and number of intersymptom

relations in which they participate, and as a consequence,

they differ in their centrality.

Conclusions

The shift to a focus on knowledge-based categorization

does not mean that the notion of similarity must be left

behind. But we do n~ed an updated approach to, and

interpretation of, similarity. The mounting evidence on

the role of theories and explanations in organizing cate-

gories is much more compatible with features at varying

levels linked by a variety of interproperty relations than

it is with independent features at a single level. In addition,

similarity may not so much constitute structure as point

toward it. There is a dimension of depth to categorization.

The conjectures about psychological essentialism may be

one way of reconciling classification in terms of perceptual

similarity or surface properties with the deeper substance

of knowledge-rich, theory-based categorization.

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